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Figure 4 in Herbicide-resistance management: a common pool resource problem?

Figure 4. Attributes of resource users associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 152).

Figure 3 in Herbicide-resistance management: a common pool resource problem?

Figure 3. Attributes of common pool resources associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 151–152).

Figure 1 in Herbicide-resistance management: a common pool resource problem?

Figure 1. Diagram of pesticide resistance as common property resource based on Miranowski and Carlson (1986). In this conceptualization, the common property resource is pest susceptibility, which is composed of a stock variable and a flow variable. Pest resistance is initially a renewable resource but becomes depleted over time through repeated use of chemicals. Thus, the actions of certain individuals may deplete the resource stock for others.

Figure 2 in Herbicide-resistance management: a common pool resource problem?

Figure 2. This diagram conceptualizes herbicide resistance as a common pool resource problem. Importantly, two conjoined common pool resources—herbicides and the weed gene pool—make up this resource system. Following common pool resource theory, this diagram illustrates the interconnectedness of four stock variables: (1) supply of a herbicide; (2) supply of a weed gene pool susceptible to a herbicide; (3) supply of a weed gene pool resistant to a herbicide; and (4) supply of herbicide efficacy on a weed gene pool. We have also diagramed corresponding flow variables or resource units (RU). In a generalized way, the use of a herbicide application (F1) influences the weed gene pool. However, the weed gene pool (S2 and S3) also acts independently of herbicide use and is influenced by both biological dynamics and social dynamics. Importantly, dynamics involving the weed gene pool are complex and include spatial and temporal variability in both the plant population and weed seedbank. The characteristics of the weed gene pool (S2 and S3) then affect the efficacy of the herbicide (S4) and whether its effectiveness is renewable or whether it becomes a finite stock resource. The quality of the herbicide (S4) may ultimately affect the supply of the herbicide (S1), if declining efficacy takes away from the herbicide's economic and chemical utility. In particular, the quality of these two common pool resources and not simply the quantity makes it a very complex resource arrangement. Factors adding complexity include that the weed gene pool is simultaneously both a pest and a resource. Furthermore, when the weed gene pool is characterized as a resource (its susceptibility to herbicides), the quality of this resource depends primarily upon provisioning practices of the common pool resource that keep the quality intact.In other words, following resource practices that do not allow internal or external resistance into the gene pool is key to maintaining its quality. The lack of quality from underprovisioning may result in a finite stock supply of the resource (i.e., weed gene pool susceptible to herbicides).Overappropriation (i.e., quantity or overharvesting of the resource) is a concern,in that it can be connected to poor provisioning practices.Aside from using a resource unit of herbicide in an application, the resource user does not directly appropriate or harvest from the system. This schematic only covers a generalized scenario, and more finescale analysis is needed to tease apart the complex relationships existing among herbicides and the weed gene pool.

Figure 7 in Interference and management of herbicide-resistant crop volunteers

Figure 7. Individual rows of weedy rice accessions or cultivated rice cultivars 8 d following a post-flood application of benzobicyclon at 371 g ai ha−1. Healthy rows are cultivated rice or resistant weedy rice accessions, whereas chlorotic rows are benzobicyclon-sensitive weedy rice accessions.

Figure 9 in Interference and management of herbicide-resistant crop volunteers

Figure 9. Field-scale evaluation of imidazolinone-resistant (ClearfieldṜ) wheat compared with non–herbicide resistant wheat (including volunteers the following year) in Saskatchewan, Canada, in the early 2000s (adapted from Beckie et al. 2011).

Figure 4 in Interference and management of herbicide-resistant crop volunteers

Figure 4. Symptoms of (A) glufosinate on glyphosate-resistant volunteer corn in glufosinate-resistant soybean and (B) sethoxydim on glyphosate/glufosinate-resistant volunteer corn in dicamba/glyphosate-resistant soybean.

Figure 2 in Interference and management of herbicide-resistant crop volunteers

Figure 2. Soybean after corn is a typical rotation in the midwestern United States. If not controlled, volunteer corn is a problem weed in soybean fields.

Figure 1 in Interference and management of herbicide-resistant crop volunteers

Figure 1. Glyphosate- and glufosinate-resistant canola volunteers in adjacent fields in Saskatchewan, Canada, due to bidirectional pollen-mediated gene flow the previous year.

Figure 3 in Interference and management of herbicide-resistant crop volunteers

Figure 3. Volunteer corn in a cornfield in Nebraska. Highly productive soils and easy access to irrigation have encouraged growers to adopt a corn-on-corn cropping system in south-central Nebraska that results in corn volunteers.

opencc-zeroNov 2023View details →

dryad36/100

Data from: Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

A key challenge in the management of populations is to quantify the impact of interven-tions in the face of environmental and phenotypic variability. However, accurate estima-tion of the effects of management and environment, in large-scale ecological research is often limited by the expense of data collection, the inherent trade-off between quality and quantity, and missing data. In this paper we develop a novel modelling framework, and demographically informed imputation scheme, to comprehensively account for the uncertainty generated by miss-ing population, management, and herbicide resistance data. Using this framework and a large dataset (178 sites over 3 years) on the densities of a destructive arable weed (Alo-pecurus myosuroides) we investigate the effects of environment, management, and evolved herbicide resistance, on weed population dynamics. In this study we quantify the marginal effects of a suite of common management prac-tices, including cropping, cultivation, and herbicide pressure, and evolved herbicide re-sistance, on weed population dynamics. Using this framework, we provide the first empirically backed demonstration that herbi-cide resistance is a key driver of population dynamics in arable weeds at regional scales. Whilst cultivation type had minimal impact on weed density, crop rotation, and earlier cultivation and drill dates consistently reduced infestation severity. Synthesis and applications: As we demonstrate that high herbicide resistance levels can produce extremely severe weed infestations, monitoring of herbicide resistance is a pri-ority for famers across western Europe. Furthermore, developing non chemical control methods is essential to control current weed populations, and prevent further resistance evolution. We recommend that planning interventions that center on crop rotation and incorporate spring sewing and cultivation to provide the best reductions in weed densi-ties. More generally, by directly accounting for missing data our framework permits the analysis of management practices with data that would otherwise be severely compro-mised.

openother-openSep 2021View details →

Supporting data and code for: A high diversity of mechanisms endows ALS-inhibiting herbicide resistance in the invasive common ragweed (Ambrosia artemisiifolia L.)

This is the first release of the final data and code for the article accepted for publication in Scientific Reports journal. It contains all the necessary scripts to produce the maps of the manuscript. All the necessary data can be found in the 'data' folder.

Figure 6 in Interference and management of herbicide-resistant crop volunteers

Figure 6. Stalk regrowth of 2,4-D-resistant cotton after shredding and treatment with duplosan.

Figure 5 in Interference and management of herbicide-resistant crop volunteers

Figure 5. Cotton stalk regrowth in a field in Georgia.

Figure 8 in Interference and management of herbicide-resistant crop volunteers

Figure 8. Volunteer soybean in a cornfield in Nebraska.